 \$9.99

# A Practical Introduction to Econometric Methods: Classical and Modern

By Patrick K. Watson, Sonja S. Teelucksingh
US\$ 9.99
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Book Description
Table of Contents
• Contents
• Foreword
• Preface
• Introduction
• Introduction
• Classical and Modern Econometrics
• Exercises
• Part I: Classical
• Chapter 1: The General Linear Regression Model
• Models in Economics and Econometrics
• Data and Econometric Models
• Specifying the Model
• Introducing the Error Term
• Desirable Properties of the Error Term
• The General Linear Regression Model
• Ordinary Least Squares
• Special Case: k = 2 and x1t = 1 for all t
• Numerical Calculation
• Forecasting with Econometric Models
• The Gauss-Markov Theorem on Least Squares
• Proof of Part (1) of the Theorem
• Proof of Part (2) of the Theorem
• Proof of Part (3) of the Theorem
• Understanding the Lessons of the Gauss-Markov Theorem
• Exercises
• Appendix 1.1: Moments of First and Second Order of Random Variables and Random Vectors
• Random Variables
• Random Matrices and Vectors
• Application to the General Linear Regression Model
• Appendix 1.2: Time Series Data for Trinidad and Tobago 1967-1991
• Chapter 2: Evaluating the Ordinary Least Squares (OLS) Regression Fit
• Some Preliminary Remarks
• The Coefficient of Determination and the Adjusted Coefficient of Determination
• Confidence Intervals for Coefficients
• Significance Tests of Coefficients
• Testing the Simultaneous Nullity of the Slope Coefficients
• “Economic” Evaluation of Regression Results
• Reporting Regression Results
• Exercises
• Chapter 3: Some Issues in the Application of the General Linear Regression Model
• Multicollinearity: the Problem
• Multicollinearity: Detection
• Multicollinearity: a Solution?
• Multicollinearity: an Illustration
• Misspecification
• Dummy Variables
• Illustration Involving a Dummy Variable
• Exercises
• Chapter 4: Generalized Least Squares, Heteroscedasticity and Autocorrelation
• Generalized Least Squares
• Properties of the Generalized Least Squares Estimator
• Consequences of Using Ordinary Least Squares When u ~ (0, s2V)
• GLS Estimation: a Practical Solution?
• Ad Hoc Procedures for the Identification of Heteroscedasticity and Autocorrelation
• Heteroscedasticity: Some Further Considerations
• Heteroscedasticity: Testing for its Presence
• The Goldfeld-Quandt Test
• The Koenker Test
• Illustration of the Koenker Test for Heteroscedasticity
• Other Tests for Heteroscedasticity
• Estimation in the Presence of Heteroscedasticity
• Autocorrelation: The Problem
• Autocorrelation: Testing for its Presence Using the Durbin-Watson Statistic
• Some Justification for the Mechanism of the Durbin-Watson Test
• An Illustration of the Durbin-Watson Test for Autocorrelation
• Other Tests for Autocorrelation
• Estimation in the Presence of Autocorrelation
• The Cochrane-Orcutt Procedure
• The Hildreth-Lu Procedure
• The EViews Procedure
• Autocorrelation and Model Specification: a Word of Caution
• Exercises
• Chapter 5: Introduction to Dynamic Models
• Dynamic Models
• Almon’s Polynomial Distributed Lag (PDL) Scheme
• Illustration of Almon’s Polynomial Distributed Lag Scheme
• The Koyck Transformation
• Illustration of the Koyck Transformation
• The Partial Adjustment Model
• The Adaptive Expectations Model
• Error Correction Mechanism (ECM) Models
• Illustration of the Error Correction Mechanism Model
• Autoregressive Distributed Lag (ADL) Models
• Illustration of the Autoregressive Distributed Lag Model
• The Durbin Test for Autocorrelation in the Presence of Lagged Endogenous Variables
• Illustration of the Durbin h-Test
• Exercises
• Chapter 6: The Instrumental Variable Estimator
• Introduction
• Consistent Estimators
• Is OLS Consistent?
• The Instrumental Variable Estimator
• The Errors in Variables Model
• Exercises
• Chapter 7: The Econometrics of Simultaneous Equation Systems
• Introduction
• Identification
• Identifiability of an Equation and Restrictions on the Structural Form
• Conditions of Identifiability of an Equation
• Estimation in Simultaneous Equation Models
• Consistency of the Two Stage Least Squares Estimator
• The Two Stage Least Squares Estimator as an Instrumental Variable Estimator
• Equivalence of Two Stage Least Squares and Indirect Least Squares in the Case of an Exactly Identified Equation
• Illustration of the Two Stage Least Squares Estimator
• Exercises
• Chapter 8: Simulation of Econometric Models
• Introduction
• Dynamic and Static Simulation
• Some Useful Summary Statistics
• Root Mean Square Error
• Mean Absolute (or Mean Difference) Error
• The Theil Inequality Coefficient
• The Theil Decomposition
• Regression and Correlation Measures
• Some Illustrations of the Use of Model Simulation
• Evaluation of Goodness-of-Fit of Single Equation Systems
• Forecasting with Single Equation Systems
• Evaluation of Goodness-of-Fit of Multiple Equation Systems
• Dynamic Response (Multiplier Analysis) in Multiple Equation Systems
• Illustration of Dynamic Response
• Forecasting and Policy Simulations with Multiple Equation Systems
• Illustration
• Exercise
• Part II: Modern
• Chapter 9: Maximum Likelihood Estimation
• Introduction
• The Cramer-Rao Lower Bound (CRLB)
• Properties of Maximum Likelihood Estimators
• Maximum Likelihood Estimation in the General Linear Regression Model
• Exercises
• Chapter 10: The Wald, Likelihood Ratio and Lagrange Multiplier Tests
• Introduction
• Defining Restrictions on the Parameter Space
• The Likelihood Ratio Test
• The Wald Test
• The Lagrange Multiplier Test
• Illustration: Test of Parameter Redundancy
• Illustration: Testing Restrictions on Coefficient Values
• Conclusion
• Exercises
• Chapter 11: Specification (and Other) Tests of Model Authenticity
• Introduction
• Ramsey’s RESET Test for Misspecification (Due to Unknown Omitted Variables)
• Illustration of the Ramsey RESET Test
• The Jarque-Bera Test for Normality
• Illustration of the Jarque-Bera Test for Normality
• The Ljung-Box and Box-Pierce Tests for White Noise
• Illustration of the Ljung-Box Test
• The White Test for Heteroscedasticity
• Illustration of the White Heteroscedasticity Test
• The Breusch-Godfrey Test for Serial Correlation
• Illustration of the Breusch-Godfrey Test for Serial Correlation
• The Chow Test for Structural Breaks
• Illustration of the Chow Test for Structural Breaks
• Exercises
• Chapter 12: Stationarity and Unit Roots
• The Concept of Stationarity
• Unit Roots: Definition
• Looking for Unit Roots: an Informal Approach
• Formal Testing for Unit Roots
• Exercises
• Chapter 13: An Introduction to ARIMA Modelling
• Introduction
• ARIMA Models
• Autoregressive Processes of Order p AR(p)
• Moving Average Processes of Order q MA(q)
• Autoregressive Moving Average Processes of Order p, q ARMA(p, q)
• Autoregressive Integrated Moving Average Processes of Order p, d, q ARIMA(p, d, q)
• The Partial Autocorrelation Function (PACF)
• Estimating the Autocorrelation and Partial Autocorrelation Functions
• Estimation of the Mean
• Estimation of the Autocovariance of Order k
• Estimation of the Autocorrelation of Order k
• Estimation of the Partial Autocorrelation of Order j
• Sampling Distributions of and
• The Box-Jenkins Iterative Cycle
• Identification
• Illustrating the Identification of p and q
• Estimation and Diagnostic Checking
• Illustration of the Estimation and Diagnostic Checking Phases
• Forecasting
• Illustration of the Forecasting Phase
• Seasonal Models
• Exercises
• Appendix 13.1
• Chapter 14: Vector Autoregression (VAR) Modelling with Some Applications
• Introduction
• Vector AutoregressiON Models
• Illustration of vector autoregression estimation using eviews
• Evaluation of Vector Autoregression Models
• The Impulse Response Function
• Variance Decomposition
• Forecasting with Vector Autoregression Models
• Illustration of Forecasting with Vector Autoregression Models
• Vector Autoregression Modelling and Causality Testing
• Testing for Causality
• Direct Granger Tests
• Illustration of Direct Granger Tests
• The Sims Test
• Exercises
• Appendix 14.1
• Chapter 15: Cointegration
• Introduction
• The Vector Error Correction Model (VECM)
• The Engle-Granger (EG) Two-Step Procedure
• Illustration of the Engle-Granger Two-Step Procedure
• Strengths and Weaknesses of the Engle-Granger Two-Step Procedure
• The Johansen Procedure
• Estimation of a and b
• Testing for the Cointegrating Rank r
• Illustration of the Johansen Procedure
• Cointegration and Causality
• Exercises
• Appendix 15.1: Critical values for ADF tests of cointegratability
• (constant term included in test equations)
• Appendices
• Statistical Tables
• References
• Index
• Untitled
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